Multi-criteria decision making tools suffer from scalability problems as the number of objectives grows. Prior art suggests methods to tackle this problem that require a priori preference or biasing information to be introduced before an optimization is performed. However, the use of a priori preference or biasing information results in sustained favoring of certain objectives leading the search to miss solutions that may have been desired over the final result. Accordingly, there is an opportunity in the industry for systems and methods for supporting restricted search in high-dimensional spaces.